Speed up your Python using Rust

What is Rust?

Rust is a systems programming language that runs blazingly fast, prevents segfaults, and guarantees thread safety.

Featuring

  • zero-cost abstractions
  • move semantics
  • guaranteed memory safety
  • threads without data races
  • trait-based generics
  • pattern matching
  • type inference
  • minimal runtime
  • efficient C bindings

Description is taken from rust-lang.org.

Why does it matter for a Python developer?

The better description of Rust I heard from Elias (a member of the Rust Brazil Telegram Group).

Rust is a language that allows you to build high level abstractions, but without giving up low-level control – that is, control of how data is represented in memory, control of which threading model you want to use etc.
Rust is a language that can usually detect, during compilation, the worst parallelism and memory management errors (such as accessing data on different threads without synchronization, or using data after they have been deallocated), but gives you a hatch escape in the case you really know what you’re doing.
Rust is a language that, because it has no runtime, can be used to integrate with any runtime; you can write a native extension in Rust that is called by a program node.js, or by a python program, or by a program in ruby, lua etc. and, however, you can script a program in Rust using these languages. — “Elias Gabriel Amaral da Silva”

There is a bunch of Rust packages out there to help you extending Python with Rust.

I can mention Milksnake created by Armin Ronacher (the creator of Flask) and also PyO3 The Rust bindings for Python interpreter.

See a complete reference list at the bottom of this article.

Let’s see it in action

For this post, I am going to use Rust Cpython, it’s the only one I have tested, it is compatible with stable version of Rust and found it straightforward to use.

NOTE: PyO3 is a fork of rust-cpython, comes with many improvements, but works only with the nightly version of Rust, so I prefered to use the stable for this post, anyway the examples here must work also with PyO3.

Pros: It is easy to write Rust functions and import from Python and as you will see by the benchmarks it worth in terms of performance.

Cons: The distribution of your project/lib/framework will demand the Rust module to be compiled on the target system because of variation of environment and architecture, there will be a compiling stage which you don’t have when installing Pure Python libraries, you can make it easier using rust-setuptools or using the MilkSnake to embed binary data in Python Wheels.

Python is sometimes slow

Yes, Python is known for being “slow” in some cases and the good news is that this doesn’t really matter depending on your project goals and priorities. For most projects, this detail will not be very important.

However, you may face the rare case where a single function or module is taking too much time and is detected as the bottleneck of your project performance, often happens with string parsing and image processing.

Example

Let’s say you have a Python function which does a string processing, take the following easy example of counting pairs of repeated chars, but have in mind that this example can be reproduced with other string processing functions or any other generally slow process in Python.

# How many subsequent-repeated group of chars are in the given string? 
abCCdeFFghiJJklmnopqRRstuVVxyZZ... {millions of chars here}
  1   2    3        4    5   6

Python is slow for doing large string processing, so you can use pytest-benchmark to compare a Pure Python (with Iterator Zipping) function versus a Regexp implementation.

# Using a Python3.6 environment
$ pip3 install pytest pytest-benchmark

Then write a new Python program called doubles.py

import re
import string
import random

# Python ZIP version
def count_doubles(val):
    total = 0
    # there is an improved version later on this post
    for c1, c2 in zip(val, val[1:]):
        if c1 == c2:
            total += 1
    return total


# Python REGEXP version
double_re = re.compile(r'(?=(.)\1)')

def count_doubles_regex(val):
    return len(double_re.findall(val))


# Benchmark it
# generate 1M of random letters to test it
val = ''.join(random.choice(string.ascii_letters) for i in range(1000000))

def test_pure_python(benchmark):
    benchmark(count_doubles, val)

def test_regex(benchmark):
    benchmark(count_doubles_regex, val)

Run pytest to compare:

$ pytest doubles.py                                                                                                           
=============================================================================
platform linux -- Python 3.6.0, pytest-3.2.3, py-1.4.34, pluggy-0.4.
benchmark: 3.1.1 (defaults: timer=time.perf_counter disable_gc=False min_roun
rootdir: /Projects/rustpy, inifile:
plugins: benchmark-3.1.1
collected 2 items

doubles.py ..


-----------------------------------------------------------------------------
Name (time in ms)         Min                Max               Mean          
-----------------------------------------------------------------------------
test_regex            24.6824 (1.0)      32.3960 (1.0)      27.0167 (1.0)    
test_pure_python      51.4964 (2.09)     62.5680 (1.93)     52.8334 (1.96)   
-----------------------------------------------------------------------------

Lets take the Mean for comparison:

  • Regexp – 27.0167 <– less is better
  • Python Zip – 52.8334

Extending Python with Rust

Create a new crate

crate is how we call Rust Packages.

Having rust installed (recommended way is https://www.rustup.rs/) Rust is also available on Fedora and RHEL repositories by the rust-toolset

I used rustc 1.21.0

In the same folder run:

cargo new pyext-myrustlib

It creates a new Rust project in that same folder called pyext-myrustlib containing the Cargo.toml (cargo is the Rust package manager) and also a src/lib.rs (where we write our library implementation).

Edit Cargo.toml

It will use the rust-cpython crate as dependency and tell cargo to generate a dylib to be imported from Python.

[package]
name = "pyext-myrustlib"
version = "0.1.0"
authors = ["Bruno Rocha <rochacbruno@gmail.com>"]

[lib]
name = "myrustlib"
crate-type = ["dylib"]

[dependencies.cpython]
version = "0.1"
features = ["extension-module"]

Edit src/lib.rs

What we need to do:

  1. Import all macros from cpython crate.
  2. Take Python and PyResult types from CPython into our lib scope.
  3. Write the count_doubles function implementation in Rust, note that this is very similar to the Pure Python version except for:
    • It takes a Python as first argument, which is a reference to the Python Interpreter and allows Rust to use the Python GIL.
    • Receives a &str typed val as reference.
    • Returns a PyResult which is a type that allows the rise of Python exceptions.
    • Returns an PyResult object in Ok(total) (Result is an enum type that represents either success (Ok) or failure (Err)) and as our function is expected to return a PyResult the compiler will take care of wrapping our Ok on that type. (note that our PyResult expects a u64 as return value).
  4. Using py_module_initializer! macro we register new attributes to the lib, including the __doc__ and also we add the count_doubles attribute referencing our Rust implementation of the function.
    • Attention to the names libmyrustlib, initlibmyrustlib, and PyInit.
    • We also use the try! macro, which is the equivalent to Python’stry.. except.
    • Return Ok(()) – The () is an empty result tuple, the equivalent of None in Python.
#[macro_use]
extern crate cpython;

use cpython::{Python, PyResult};

fn count_doubles(_py: Python, val: &str) -> PyResult<u64> {
    let mut total = 0u64;

    // There is an improved version later on this post
    for (c1, c2) in val.chars().zip(val.chars().skip(1)) {
        if c1 == c2 {
            total += 1;
        }
    }

    Ok(total)
}

py_module_initializer!(libmyrustlib, initlibmyrustlib, PyInit_myrustlib, |py, m | {
    try!(m.add(py, "__doc__", "This module is implemented in Rust"));
    try!(m.add(py, "count_doubles", py_fn!(py, count_doubles(val: &str))));
    Ok(())
});

Now let’s build it with cargo

$ cargo build --release
    Finished release [optimized] target(s) in 0.0 secs

$ ls -la target/release/libmyrustlib*
target/release/libmyrustlib.d
target/release/libmyrustlib.so*  <-- Our dylib is here

Now let’s copy the generated .so lib to the same folder where our doubles.py is located.

NOTE: on Fedora you must get a .so in other system you may get a .dylib and you can rename it changing extension to .so.

$ cd ..
$ ls
doubles.py pyext-myrustlib/

$ cp pyext-myrustlib/target/release/libmyrustlib.so myrustlib.so

$ ls
doubles.py myrustlib.so pyext-myrustlib/

Having the myrustlib.so in the same folder or added to your Python path allows it to be directly imported, transparently as it was a Python module.

 

Importing from Python and comparing the results

Edit your doubles.py now importing our Rust implemented version and adding a benchmark for it.

import re
import string
import random
import myrustlib   #  <-- Import the Rust implemented module (myrustlib.so)


def count_doubles(val):
    """Count repeated pair of chars ins a string"""
    total = 0
    for c1, c2 in zip(val, val[1:]):
        if c1 == c2:
            total += 1
    return total


double_re = re.compile(r'(?=(.)\1)')


def count_doubles_regex(val):
    return len(double_re.findall(val))


val = ''.join(random.choice(string.ascii_letters) for i in range(1000000))


def test_pure_python(benchmark):
    benchmark(count_doubles, val)


def test_regex(benchmark):
    benchmark(count_doubles_regex, val)


def test_rust(benchmark):   #  <-- Benchmark the Rust version
    benchmark(myrustlib.count_doubles, val)

Benchmark

$ pytest doubles.py
==============================================================================
platform linux -- Python 3.6.0, pytest-3.2.3, py-1.4.34, pluggy-0.4.
benchmark: 3.1.1 (defaults: timer=time.perf_counter disable_gc=False min_round
rootdir: /Projects/rustpy, inifile:
plugins: benchmark-3.1.1
collected 3 items

doubles.py ...


-----------------------------------------------------------------------------
Name (time in ms)         Min                Max               Mean          
-----------------------------------------------------------------------------
test_rust              2.5555 (1.0)       2.9296 (1.0)       2.6085 (1.0)    
test_regex            25.6049 (10.02)    27.2190 (9.29)     25.8876 (9.92)   
test_pure_python      52.9428 (20.72)    56.3666 (19.24)    53.9732 (20.69)  
-----------------------------------------------------------------------------

Lets take the Mean for comparison:

  • Rust – 2.6085 <– less is better
  • Regexp – 25.8876
  • Python Zip – 53.9732

Rust implementation can be 10x faster than Python Regex and 21x faster than Pure Python Version.

Interesting that Regex version is only 2x faster than Pure Python 🙂

NOTE: That numbers makes sense only for this particular scenario, for other cases that comparison may be different.

Updates and Improvements

After this article has been published I got some comments on r/python and also on r/rust

The contributions came as Pull Requests and you can send a new if you think the functions can be improved.

Thanks to: Josh Stone we got a better implementation for Rust which iterates the string only once and also the Python equivalent.

Thanks to: Purple Pixie we got a Python implementation using itertools, however this version is not performing any better and still needs improvements.

Iterating only once

fn count_doubles_once(_py: Python, val: &str) -> PyResult<u64> {
    let mut total = 0u64;

    let mut chars = val.chars();
    if let Some(mut c1) = chars.next() {
        for c2 in chars {
            if c1 == c2 {
                total += 1;
            }
            c1 = c2;
        }
    }

    Ok(total)
}
def count_doubles_once(val):
    total = 0
    chars = iter(val)
    c1 = next(chars)
    for c2 in chars:
        if c1 == c2:
            total += 1
        c1 = c2
    return total

Python with itertools

import itertools

def count_doubles_itertools(val):
    c1s, c2s = itertools.tee(val)
    next(c2s, None)
    total = 0
    for c1, c2 in zip(c1s, c2s):
        if c1 == c2:
            total += 1
    return total

Why not C/C++/Nim/Go/Ĺua/PyPy/{other language}?

Ok, that is not the purpose of this post, this post was never about comparing Rust X other language, this post was specifically about how to use Rust to extend and speed up Python and by doing that it means you have a good reason to choose Rust instead of other language or by its ecosystem or by its safety and tooling or just to follow the hype, or simply because you like Rust doesn’t matter the reason, this post is here to show how to use it with Python.

I (personally) may say that Rust is more future proof as it is new and there are lots of improvements to come, also because of its ecosystem, tooling, and community and also because I feel comfortable with Rust syntax, I really like it!

So, as expected people started complaining about the use of other languages and it becomes a sort of benchmark, and I think it is cool!

So as part of my request for improvements some people on Hacker News also sent ideas, martinxyz sent an implementation using C and SWIG that performed very well.

C Code (swig boilerplate omitted)

uint64_t count_byte_doubles(char * str) {
  uint64_t count = 0;
  while (str[0] && str[1]) {
    if (str[0] == str[1]) count++;
    str++;
  }
  return count;
}

And our fellow Red Hatter Josh Stone improved the Rust implementation again by replacing chars with bytes so it is a fair competition with C as C is comparing bytes instead of Unicode chars.

fn count_doubles_once_bytes(_py: Python, val: &str) -> PyResult<u64> {
    let mut total = 0u64;

    let mut chars = val.bytes();
    if let Some(mut c1) = chars.next() {
        for c2 in chars {
            if c1 == c2 {
                total += 1;
            }
            c1 = c2;
        }
    }

    Ok(total)
}

There are also ideas to compare Python list comprehension and numpy so I included here

Numpy:

import numpy as np

def count_double_numpy(val):
    ng=np.fromstring(val,dtype=np.byte)
    return np.sum(ng[:-1]==ng[1:])

List comprehension

def count_doubles_comprehension(val):
    return sum(1 for c1, c2 in zip(val, val[1:]) if c1 == c2)

The complete test case is on repository test_all.py file.

New Results

NOTE: Have in mind that the comparison was done in the same environment and may have some differences if run in a different environment using another compiler and/or different tags.

-------------------------------------------------------------------------------------------------
Name (time in us)                     Min                    Max                   Mean          
-------------------------------------------------------------------------------------------------
test_rust_bytes_once             476.7920 (1.0)         830.5610 (1.0)         486.6116 (1.0)    
test_c_swig_bytes_once           795.3460 (1.67)      1,504.3380 (1.81)        827.3898 (1.70)   
test_rust_once                   985.9520 (2.07)      1,483.8120 (1.79)      1,017.4251 (2.09)   
test_numpy                     1,001.3880 (2.10)      2,461.1200 (2.96)      1,274.8132 (2.62)   
test_rust                      2,555.0810 (5.36)      3,066.0430 (3.69)      2,609.7403 (5.36)   
test_regex                    24,787.0670 (51.99)    26,513.1520 (31.92)    25,333.8143 (52.06)  
test_pure_python_once         36,447.0790 (76.44)    48,596.5340 (58.51)    38,074.5863 (78.24)  
test_python_comprehension     49,166.0560 (103.12)   50,832.1220 (61.20)    49,699.2122 (102.13) 
test_pure_python              49,586.3750 (104.00)   50,697.3780 (61.04)    50,148.6596 (103.06) 
test_itertools                56,762.8920 (119.05)   69,660.0200 (83.87)    58,402.9442 (120.02) 
-------------------------------------------------------------------------------------------------
  • The new Rust implementation comparing bytes is 2x better than the old comparing Unicode chars
  • The Rust version is still better than the C using SWIG
  • Rust comparing unicode chars is still better than numpy
  • However Numpy is better than the first Rust implementation which had the problem of double iteration over the unicode chars
  • Using a list comprehension does not make significative difference than using pure Python

NOTE: If you want to propose changes or improvements send a PR here: https://github.com/rochacbruno/rust-python-example/

Conclusion

Back to the purpose of this post “How to Speed Up your Python with Rust” we started with:

– Pure Python function taking 102 ms.
– Improved with Numpy (which is implemented in C) to take 3 ms.
– Ended with Rust taking 1 ms.

In this example Rust performed 100x faster than our Pure Python.

Rust will not magically save you, you must know the language to be able to implement the clever solution and once implemented in the right it worth as much as C in terms of performance and also comes with amazing tooling, ecosystem, community and safety bonuses.

Rust may not be yet the general purpose language of choice by its level of complexity and may not be the better choice yet to write common simple applications such as web sites and test automation scripts.

However, for specific parts of the project where Python is known to be the bottleneck and your natural choice would be implementing a C/C++ extension, writing this extension in Rust seems easy and better to maintain.

There are still many improvements to come in Rust and lots of others crates to offer Python <--> Rust integration. Even if you are not including the language in your tool belt right now, it is really worth to keep an eye open to the future!

References

The code snippets for the examples showed here are available in GitHub repo: https://github.com/rochacbruno/rust-python-example.

The examples in this publication are inspired by Extending Python with Rust talk by Samuel Cormier-Iijima in Pycon Canada. video here: https://www.youtube.com/watch?v=-ylbuEzkG4M.

Also by My Python is a little Rust-y by Dan Callahan in Pycon Montreal. video here: https://www.youtube.com/watch?v=3CwJ0MH-4MA.

Other references:

Join Community:

Join Rust community, you can find group links in https://www.rust-lang.org/en-US/community.html.

If you speak Portuguese, I recommend you to join https://t.me/rustlangbr and there is the http://bit.ly/canalrustbr on Youtube.

Author

Bruno Rocha

  • Senior Quality Engineer at Red Hat
  • Teaching Python and Flask at CursoDePython.com.br
  • Fellow Member of Python Software Foundation
  • Member of RustBR study group

More info: http://about.me/rochacbruno and http://brunorocha.org


Whether you are new to Containers or have experience, downloading this cheat sheet can assist you when encountering tasks you haven’t done lately.

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  • I’m curious, does the Rust &str work fine with Python 3 str when it comes to unicode? I.e. is it stored in the same way? Does the Rust library operate on the same data, or is the text being copied to different format and then back?

    • According to https://docs.python.org/3/c-api/unicode.html Rust reads a pointer to the same data. In the case of rust-cpthon it supports ascii and utf-8, on PyO3 only unicode is supported. However if the string is mutable and changed on Rust side a new compatible string will be returned to Python.

  • Abiatha Swelter

    Does it offer a performance improvement relative to implementing the same function in Cython? Or any other advantage?

    • My guess is that the performance will be the same, I have not tried to write the same function in C, I will give a try and add to the post.

  • Mamy Ratsimbazafy

    Another alternative is Nim, here is a presentation of optimizing Python with Nim at PyGotham 2017.

    The main advantage is that Nim syntax though typed, is very similar to Python and Rust learning curve is quite high.

  • Stu

    It would be great to see how some other implementations, like Pypy compare.

  • There are new results using Numba and Cython on the repository https://github.com/rochacbruno/rust-python-example#new-results

  • Bo Nielsen

    This was just what I needed for my bachelor project on “edge computing ETL with Serverless architecture and the economical benefits”.